Trust Incident Account Model: Preliminary Indicators for Trust Rhetoric and Trust or Distrust in Blogs
Bibliographic record
Abstract
This paper defines a concept of trust incident accounts as verbal reports of empirical episodes in which a trustor has reached a state of positive or negative expectations of a trustee’s behavior under associated risks. Such expectations are equated to trust and distrust, correspondingly, and present a sharp contrast with hypocritical use of trust rhetoric with ulterior motives such as an attempt to manipulate readers or gain trustworthiness. Distinguishing the three: trust, distrust, and trust rhetoric, is formulated as a new challenge in sentiment analysis and opinion-mining. Based on a preliminary exploration of trust narratives in blogs, 14 categories of textual indicators were identified manually. The finer-grain analytical model of trust incident accounts is proposed to include 12 information extraction frame components: trustor, trustee, source, textual clue, trust valence, risks, reasons, actions, trustor-trustee relationship, narrow context, broad domain, and complements. The study draws a cross-disciplinary theoretical bridge from social science and information technology trust literature to opinion-mining, and emphasizes the value of understanding trust in longer-term social relations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".